An International University Partnership to Support the Social Service Workforce and Strengthen the Child Protection System in Ghana
Bibliographic record
Abstract
Common perceptions of children in Africa tend to focus on poverty, famine, and war. There has been less attention given to the successes of African countries that have aimed to improve the lives of children. One such success is Ghana, which is a regional and continental leader in child protection issues and continues to nurture a robust social service workforce. Yet, there exists some detachment in the implementation of child welfare laws and practice. To address this gap, there has been a recent global movement to address child protection issues by strengthening child protection systems and specifically social work education. This chapter uses a process evaluation to examine an international university partnership between a Global North Canadian university and a Global South Ghanaian university to integrate child protection knowledge into the university curriculum and to produce the next generation of social service workers. This chapter chronicles the development and structure of the partnership, discusses the opportunities and challenges faced, and suggests a future agenda to support the endogenous strengths inherent within Ghanaian universities to work towards improving the status of children and families and to serve as a model for other African contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".